QuantaAlpha：基於大語言模型的阿爾法因子挖掘演化框架

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

February 6, 2026

作者: Jun Han, Shuo Zhang, Wei Li, Zhi Yang, Yifan Dong, Tu Hu, Jialuo Yuan, Xiaomin Yu, Yumo Zhu, Fangqi Lou, Xin Guo, Zhaowei Liu, Tianyi Jiang, Ruichuan An, Jingping Liu, Biao Wu, Rongze Chen, Kunyi Wang, Yifan Wang, Sen Hu, Xinbing Kong, Liwen Zhang, Ronghao Chen, Huacan Wang

cs.AI

摘要

金融市場具有高噪聲與非平穩特性，使得阿爾法因子挖掘對回測結果中的噪聲及市場狀態突變極度敏感。儘管近期出現的智能體框架提升了阿爾法挖掘的自動化程度，但其往往缺乏可控的多輪次搜索機制與驗證經驗的可靠複用。為應對這些挑戰，我們提出QuantaAlpha——一種將每次端到端挖掘過程視為軌跡的演化式阿爾法挖掘框架，通過軌跡層級的變異與交叉操作優化因子。該框架能定位軌跡中的次優步驟進行靶向修正，並重組互補的高收益片段以複用有效模式，實現結構化的迭代探索與精煉。在因子生成過程中，QuantaAlpha確保假設、因子表達式與可執行代碼間的語義一致性，同時約束生成因子的複雜度與冗餘性以緩解擁擠效應。基於滬深300指數的廣泛實驗表明，該框架相較強基線模型與現有智能體系統均取得穩定增益。使用GPT-5.2時，QuantaAlpha的信息係數（IC）達0.1501，年化收益率（ARR）為27.75%，最大回撤（MDD）僅7.98%。此外，在滬深300上挖掘的因子可有效遷移至中證500指數與標普500指數，四年累計超額收益分別達160%與137%，展現出QuantaAlpha在市場分佈變化下的強健性。

English

Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.